This study explores the application of Generative Artificial Intelligence (AI) in Natural Language Processing (NLP) for cybersecurity vulnerability detection. Traditional approaches, such as Common Vulnerabilities and Exposures (CVE) and Common Vulnerability Scoring System (CVSS), are slow and manual, limiting their ability to address rapidly evolving threats. By leveraging generative AI models, this research demonstrates NLP’s ability to process large-scale unstructured textual data from Open-Source Intelligence (OSINT) platforms to detect and prioritize vulnerabilities in real time. The study highlights techniques like sentiment analysis, topic modelling, and text classification, alongside machine learning models such as BERT and neural networks. Results emphasize the transformative potential of generative AI in enhancing cybersecurity resilience and mitigating risks.